In this section we apply our results for a general SDNN to a stochastic version of the feedforward neural network with ReLU activation (FNNRA)
introduced in [1]}. The key insight in [1]} is that ideas from tropical algebra can be used to study feedforward neural networks, especially with ReLU activations. The intuition is that the activation function of a feedforward neural network requires computing a maximum, which turns out to correspond to tropical addition.
